Tootfinder

LOTUS: A Leaderboard for Detailed Image Captioning from Quality to Societal Bias and User Preferences
Yusuke Hirota, Boyi Li, Ryo Hachiuma, Yueh-Hua Wu, Boris Ivanovic, Yuta Nakashima, Marco Pavone, Yejin Choi, Yu-Chiang Frank Wang, Chao-Han Huck Yang
https://arxiv.org/abs/2507.19362

LOTUS: A Leaderboard for Detailed Image Captioning from Quality to Societal Bias and User Preferences
Large Vision-Language Models (LVLMs) have transformed image captioning, shifting from concise captions to detailed descriptions. We introduce LOTUS, a leaderboard for evaluating detailed captions, addressing three main gaps in existing evaluations: lack of standardized criteria, bias-aware assessments, and user preference considerations. LOTUS comprehensively evaluates various aspects, including caption quality (e.g., alignment, descriptiveness), risks (\eg, hallucination), and societal biases …

Alaskan whaling captains score special tax cut in GOP megabill (Brian Faler/Politico)
https://www.politico.com/live-updates/2025/06/28/congress/alaskan-whaling-captains-score-special-tax-cut-in-gop-megabill-00431077
http://www.memeorandum.com/250628/p37#a250628p37

🇺🇦 #NowPlaying on KEXP's #Early
Billy Joel:
🎵 Captain Jack
#BillyJoel
https://plainseltzer.bandcamp.com/track/captain-jack-billy-joel
https://open.spotify.com/track/3igkLltDaiFiMl83IdDBfm

Exascale Implicit Kinetic Plasma Simulations on El~Capitan for Solving the Micro-Macro Coupling in Magnetospheric Physics
Stefano Markidis, Andong Hu, Ivy Peng, Luca Pennati, Ian Lumsden, Dewi Yokelson, Stephanie Brink, Olga Pearce, Thomas R. W. Scogland, Bronis R. de Supinski, Gian Luca Delzanno, Michela Taufer
https://arxiv.org/abs/2507.…

Exascale Implicit Kinetic Plasma Simulations on El~Capitan for Solving the Micro-Macro Coupling in Magnetospheric Physics
Our fully kinetic, implicit Particle-in-Cell (PIC) simulations of global magnetospheres on up to 32,768 of El Capitan's AMD Instinct MI300A Accelerated Processing Units (APUs) represent an unprecedented computational capability that addresses a fundamental challenge in space physics: resolving the multi-scale coupling between microscopic (electron-scale) and macroscopic (global-scale) dynamics in planetary magnetospheres. The implicit scheme of iPIC3D supports time steps and grid spacing that a…

BEV-LLM: Leveraging Multimodal BEV Maps for Scene Captioning in Autonomous Driving
Felix Brandstaetter, Erik Schuetz, Katharina Winter, Fabian Flohr
https://arxiv.org/abs/2507.19370

BEV-LLM: Leveraging Multimodal BEV Maps for Scene Captioning in Autonomous Driving
Autonomous driving technology has the potential to transform transportation, but its wide adoption depends on the development of interpretable and transparent decision-making systems. Scene captioning, which generates natural language descriptions of the driving environment, plays a crucial role in enhancing transparency, safety, and human-AI interaction. We introduce BEV-LLM, a lightweight model for 3D captioning of autonomous driving scenes. BEV-LLM leverages BEVFusion to combine 3D LiDAR poi…

Mitigating Audiovisual Mismatch in Visual-Guide Audio Captioning
Le Xu, Chenxing Li, Yong Ren, Yujie Chen, Yu Gu, Ruibo Fu, Shan Yang, Dong Yu
https://arxiv.org/abs/2505.22045

Mitigating Audiovisual Mismatch in Visual-Guide Audio Captioning
Current vision-guided audio captioning systems frequently fail to address audiovisual misalignment in real-world scenarios, such as dubbed content or off-screen sounds. To bridge this critical gap, we present an entropy-aware gated fusion framework that dynamically modulates visual information flow through cross-modal uncertainty quantification. Our novel approach employs attention entropy analysis in cross-attention layers to automatically identify and suppress misleading visual cues during mo…

I’ve been thoroughly enjoying #CaptainOfIndustry lately. If you’re a fan of #Factorio I recommend giving it a go.

Time For 9 o'clock #HashTagGames hosted by @… Let's play!
#MovieSubPlots

SynC: Synthetic Image Caption Dataset Refinement with One-to-many Mapping for Zero-shot Image Captioning
Si-Woo Kim, MinJu Jeon, Ye-Chan Kim, Soeun Lee, Taewhan Kim, Dong-Jin Kim
https://arxiv.org/abs/2507.18616

SynC: Synthetic Image Caption Dataset Refinement with One-to-many Mapping for Zero-shot Image Captioning
Zero-shot Image Captioning (ZIC) increasingly utilizes synthetic datasets generated by text-to-image (T2I) models to mitigate the need for costly manual annotation. However, these T2I models often produce images that exhibit semantic misalignments with their corresponding input captions (e.g., missing objects, incorrect attributes), resulting in noisy synthetic image-caption pairs that can hinder model training. Existing dataset pruning techniques are largely designed for removing noisy text in…

Dense Video Captioning using Graph-based Sentence Summarization
Zhiwang Zhang, Dong Xu, Wanli Ouyang, Luping Zhou
https://arxiv.org/abs/2506.20583 https://…

Dense Video Captioning using Graph-based Sentence Summarization
Recently, dense video captioning has made attractive progress in detecting and captioning all events in a long untrimmed video. Despite promising results were achieved, most existing methods do not sufficiently explore the scene evolution within an event temporal proposal for captioning, and therefore perform less satisfactorily when the scenes and objects change over a relatively long proposal. To address this problem, we propose a graph-based partition-and-summarization (GPaS) framework for d…